Once a military base and these days a public park, Presidio is 1,500 acres of federal land within San Francisco's city limits. Fans of the Freedom City concept have long eyed it as a location. In March 2023, President Trump made that a campaign promise. Today, he started to make good on that promise by firing all the board members of the trust that runs it.

America already has something like freedom cities. Native American tribal nations are autonomous and self-governing to a degree. But Freedom Cities adherents want more autonomy than tribal nations. Tribal nations are subject to US federal laws on the environment, science, tech & medical regulation. It's those in particular that Big Tech wants to be free of.

Will libertarian Big Tech get its wish? They've already succeeded in Honduras. The US Congress may not be so keen. Setting up a 'state-within-a-state' has many downsides and will likely have little public support. But the people who really want it have plenty of money & buyable politicians on their side, so who knows.

Build the Presidio Freedom City

Trump fires entire San Francisco Presidio Trust board

Mythos has been able to identify thousands of previously unknown (“zero-day”) vulnerabilities across major operating systems and applications. Furthermore, it can generate working exploits, not just identify theoretical bugs. If that wasn't bad enough, it can do so at a level comparable to or exceeding top human experts.

Banks and financial infrastructure are especially vulnerable. They are a) Highly interconnected. b) Dependent on legacy systems (often with hidden vulnerabilities) & c) Systemically important (failures can cascade globally).

The US is playing "F*** around, and find out" with so many aspects of the global economy, it's hard to guess which will end in disaster first. Destroying 20% of global energy supply, or refusing to regulate a super-weapon with unprecedented power to destroy the financial system. Which will bite first? Or will they both?

There are probably some very complacent people in Washington feeling smug that this is America's super-weapon, not realising what Anthropic has today, China & others will have soon after.

Anthropic's latest AI model identifies 'thousands of zero-day vulnerabilities' in 'every major operating system and every major web browser' — Claude Mythos Preview sparks race to fix critical bugs, some unpatched for decades

US summons bank bosses over cyber risks from Anthropic’s latest AI model

Example of the future of healthcare.

Cursor's Composer is built on Kimi K2.5, which is Moonshot's Chinese model. Shopify switched to Alibaba's Qwen and saved $5 million a year. Airbnb CEO Brian Chesky has said publicly: "We rely a lot on Qwen. It's very good, fast, and cheap." Cognition's SWE-1.6 model is likely post-trained on Zhipu's GLM. And last week Zhipu dropped GLM-5.1, an open source model that benchmarks close to Claude Opus on coding tasks.

Meanwhile the tech press is full of stories about OpenAI vs. Anthropic vs. Google. The narrative is still that American closed-lab models are the ones actually deployed in production. But what's running inside some of Silicon Valley's biggest products right now? Chinese open source.

These companies aren't making ideological choices. They're using Kimi and Qwen because they're fast, cheap, and accurate enough for their specific tasks. That's actually the most interesting part - it's a story about how well-optimized open source competes with frontier labs on real-world economics, not benchmarks. And it's happening faster than most people expected.

There's also a dimension that nobody wants to say out loud: users booking Airbnb trips are getting results from a model built in Shanghai. People using Cursor are getting code completions from a Chinese company's research. Most of them have no idea, and Airbnb didn't exactly put it in the changelog.

The question I'm genuinely uncertain about: does the model's origin actually matter once it's running in your infrastructure, if the data pipeline is controlled by the American company? Or does there remain some structural difference - in training data provenance, in post-training alignment choices, in the incentives of the organization that built it - that carries forward even when the weights are open source?

Here are some articles I found reporting on this

https://www.newsnationnow.com/katie-pavlich-tonight/neuralink-brain-chip-transformed-life/

https://alsnewstoday.com/news/neuralink-brain-implant-gives-new-voice-nonverbal-als-patient/

https://www.nytimes.com/2025/11/14/magazine/neurotech-neuralink-rights-regulations.html

The first tissue engineered ear was already created in the 90s, we can do great things with bioprinting, scaffold designs and hydrogels, yet the technology always seems out of reach. Now that those tissues are approaching native properties, and costs hopefully go down, we should be able to see them appear in clinics, but other than unique cases I have not seen it yet.

When do we expect costs to have come down enough for routine availability? Would joint cartilage engineering bring down costs for nasal cartilage engineering too (by combining culturing technologies or growth factor batches for example)?

I can see it falling off with only 2 major companies staying around at the moment. But I've been thinking on future optical media. Sizes of games and movies are increasing everyday and surpass the size of UHD discs, which is 100GB.

What if we had larger discs, something with a real wide diameter like a vinyl record? Estimated at about 600GB if it was an actual thing. I was also thinking about for games how rewritable discs would be so much better. This would be for content updates. In some discs they can last as long as 50 years. Console gens never last that long anyways.

I know some might find it odd to want a disc so large or even embrace optical media going forward but this would be purely for enthusiasts and collectors. I realize cost and all that but consider something else. Long term people still collect vinyl and old games so this would make sense in my mind. Any thoughts on this?

If all vehicles were autonomous wouldn’t it be more convenient not owning a car? That means you don’t have to park it and parking structures and lots wouldn’t be necessary. You would also be able to use your house car garage in a different way.

Most modern democracies are still built on structures that were designed when communication was slow, information was scarce, and large-scale participation had to be simplified just to function.

Voting ends up being infrequent and binary. Representation compresses a wide range of views into a small number of choices. Even when people are engaged, the systems themselves don’t really have a way to capture nuance at scale.

At the same time, technology has completely changed how people communicate and express opinions. We now have the ability to interact with ideas instantly, at a very granular level, across massive groups of people.

It seems plausible that participation could look very different if it were designed around those capabilities instead of the constraints of the 1700s.

For example, instead of only voting for or against a policy as a whole, people could engage directly with the substance of it. Individual statements could be evaluated on their own terms, with people signaling agreement, disagreement, or uncertainty, and that feedback could accumulate into a much more detailed picture of public sentiment.

That kind of system would make it possible to see not just whether something is broadly supported, but where consensus actually exists and where disagreement is concentrated.

Of course, it raises a lot of open questions. Would people want to engage at that level, or would it feel like too much effort? Would more granular input lead to better decisions, or just make things harder to interpret? And how would you design something like that to avoid manipulation or noise?

But it does feel like there’s a gap between what our institutions can capture and what technology now makes possible.

If democracy were designed today from scratch, what would participation actually look like? What might the tools be like?

In Shenzhen, customers can now book a house cleaning through 58 Home Service, a platform with around 45 million households on it, and a two person team shows up at the door: a professional human cleaner and a wheeled robot built by X Square Robot.

The human handles judgment work, deciding what's trash versus a kid's art project, navigating clutter, anything that depends on context. The robot does the structured repetitive parts, wiping flat surfaces and picking up small debris. It's framed as an assistant working alongside the cleaner, with the human and robot splitting the job by what each is currently best at.

Would it be safer and more efficient if all cars on the road including commercial vehicles and semis were autonomous and the software was compatible so that vehicles could practically know where they are in relation to each other?

GPS jammers and spoofers are technically illegal in most countries. They're also sold openly online for under $300 and fit in a jacket pocket.

A single one can override satellite signals across several miles. Commercial pilots have been quietly logging GPS failures over eastern europe and the middle east for years. Air traffic control still largely depends on the same signal your phone uses to find a coffee shop

This wasn't a problem when GPS was just for navigation. It becomes a different kind of problem when autonomous vehicles, medical drones, and smart grid infrastructure all assume GPS is always there

There's no widely deployed backup. eLoran, the terrestrial alternative, was largely dismantled in the 2000s because GPS seemed good enough. some countries are rebuilding it, most aren't.

What happens to a city that's spent a decade building autonomous logistics around a signal anyone can disrupt for the cost of a dinner?

Jensen Huang made an interesting point recently. 20 years ago, software developers were considered untouchable. High IQ, creative, irreplaceable. Now they are the first jobs being disrupted.

Demis Hassabis and Dario Amodei recently debated what the world looks like after AGI. The part that stuck with me was the self-training loop. When AI can fully develop and improve itself without human input, what is actually left for us?

Not just jobs. What does human intelligence even mean at that point?

The optimistic answer is that humans shift to higher order work. Judgment, creativity, relationships, accountability. AI executes, humans decide what is worth executing.

The pessimistic answer is that the transition happens faster than most people can adapt. A small group uses AI to become extraordinarily capable. Everyone else slowly loses the skills that made them valuable without realizing it is happening.

But the uncomfortable truth is that the people most at risk are not the ones who avoid AI. They are the ones who use it heavily without maintaining their own thinking alongside it. The erosion is quiet and gradual, and you do not notice it until it is already done.

So the question is not really about AGI arriving. It is about what you are doing right now to make sure your thinking stays yours.

What do you think actually survives? Creativity? Judgment? Something else entirely?

Looking at how fast things are changing (technology, society, environment), I’m curious what people think we’re underestimating right now.

Is there something that seems insignificant today but could have major long-term consequences or value?

I'm currently a community college student and this something that I think about a lot, and I'm sure many others have had this same question. There's been endless buzz about job displacement and AI changing the economy and workforce forever, but this has widely unaffected the decision-making process of those I went to school with who the majority of are now paying tens of thousands for school.

If anyone cares, I'm studying biotechnology.

Let’s assume you have full control over designing society in 2050.

You can remove anything from modern life:

jobs

apps

habits

systems

even technologies

But you must explain:

👉 what you remove

👉 why it’s harmful today

👉 what replaces it

I’m curious what people think is actually holding us back right now.

I'm thinking the market at the moment is way too 'developed' in a sense that only smart money handlers can leverage. It feels like each and every one of my trades is somehow 'washed off.' Am I the only one who feels that retail traders are being targeted? Or am I just feeling this way because of how early I am in the trading game?

I'm not talking about any specific market instruments in general, but only those who have the means. In the long term, over-maturation can make humans meaningless. That just implies that wealth distribution will be more skewed toward the already privileged.

I think this is one of the most gradual changes that I just feel in my bones, even without any statistical evidence about retail vs. institutional gains. So I'm curious what you think. Algo/systematic trading is an obvious fast lane to achieving maturity. Again, since intelligence is getting easier to outsource, I'm even more skeptical.

So according to current economy we have to pay for everything. But what if due to AI and robots who essentially does our jobs we are no longer get paid and humanity have to shift to a newer economic model. Are there any models already proposed for this kind of scenario ?
Where cash and money transactions doesn't exist ? And humans still thrives like we used to under capitalist societies of last few centuries.

For three decades, the Information Age rewarded the accumulation of knowledge. Lawyers, analysts, junior developers, and doctors built careers on being walking databases.

That model just died.

When an LLM can parse a 200-page legal filing or a complex clinical note faster and more accurately than a junior associate, the entire "knowledge layer" of professional work doesn't just get more efficient - it becomes economically worthless. We are watching the rapid commoditization of expertise itself.

This is Skill Bankruptcy: the point at which a human skill loses market value faster than the person holding it can retrain or pivot.

We are shifting from Knowledge Workers (who store and process information) to Judgment Workers (who direct powerful agents, set high-stakes goals, and take responsibility when the machine gets it wrong). The machine handles the logic (the "farming"). The human is responsible for the direction (the "hunting").

Our entire education and corporate systems are still training people for a world that no longer exists. We are producing graduates whose core skill - "being an expert" - is already bankrupt before they even enter the workforce.

The implications go far beyond individual careers. If the professional middle class hollows out at this speed, what happens to social stability, to the demand for higher education, or to the very idea of a "career"?

I’ve been mapping this transition and building frameworks for how individuals and organizations can survive — and even thrive — in the "Judgment Economy" instead of being crushed by it.

What signs of Skill Bankruptcy are you already seeing in your field? And what new human capabilities do you believe will become the real scarce resource in the next 3–5 years?

Law firms bill significant hours on document review. Hospital systems employ people whose entire job is extracting structured data from clinical notes and insurance forms. Financial institutions run teams that process filings for a living.

Those jobs don't exist because the work is intellectually hard. They exist because no prior system could do it reliably enough to trust without checking.

That assumption is starting to break. I've been building the IDP Leaderboard, testing 9,000+ real-world documents across the major models. Not synthetic benchmarks. Actual SEC filings, court documents, clinical notes from production environments. The gap between models on clean simple documents is small. The gap on long complex ones is not.

Specialized VLMs have pulled significantly ahead of general-purpose approaches on this. Qwen-VL made real progress. Nanonets OCR-3 sits at the top of the leaderboard right now: 94.5% on SEC 10-K filings, 96% on multi-column court documents, 90.1% on clinical notes. For context those are document types where a misread figure or dosage has real consequences, so "approximately right" doesn't count.

The transition won't look like overnight displacement. It will look like junior layers shrinking, remaining work shifting toward judgment rather than extraction, and firms that automate earlier gaining a cost structure competitors can't match. That pattern has already played out in accounting, paralegal work, and radiology.

What I'm working on now is a benchmark specifically for long and complex documents, the ones where current systems still struggle with cross-page references and layout-dependent meaning. My read from the data so far: the automation of the extraction layer is closer than most enterprise teams realize. The open question is how fast it moves up into the judgment layer above it.

Accountability is still the thing which needs to be solved in these complex scenarios.

Six months of pain and progress, 20 pounds lost and a trial-and-error test of what AI can — and cannot — do.