»Slow down AI« sounds noble until you ask who actually slows down: Democracies and companies that comply simply hand the advantage to rivals that don’t. History suggests voluntary technological restraint rarely wins against geopolitical competition. The race doesn’t stop: it just changes leaders.

Exactly. Platform loyalty is a bad archiving strategy. If one platform disappears, changes the rules, or buries old content, your work and links still survive instead of vanishing with someone else’s business decisions.

The irony is that this argument will probably get more attention on Reddit than on any open forum: Centralised platforms are great for discovery and awful for preservation. Use them as the billboard, not the library. Keep the valuable stuff where you control the rules, the backups and the future.

A $250 billion backstop is an extraordinary amount of financial engineering for an industry that’s supposed to be the next industrial revolution: If AI demand is as inevitable as claimed, why does every new data centre seem to require increasingly creative financing and ever larger guarantees?

Because it was always so wonderfully easy to do nothing. Costs have risen, and new realities are increasingly being created in both politics and the economy: in France, Denmark, Schleswig-Holstein and so many other places, large and small.

Without a doubt. And yet: without open source, pretty much everything digital would come to an end straight away. Sometimes what’s visible isn’t what really matters.

We’ve spent years telling kids that every problem is solved by putting another screen in front of them. Now we’re discovering that unlimited access to the world’s biggest distraction might not have been the revolutionary education strategy we thought it was. Technology is a tool, not a teaching method.

Ever since facts have been replaced by ideologies, the obvious has increasingly become a mystery.

Absolutely. Trust isn’t binary, and it isn’t unique to Chinese models either. Every (frontier) model reflects the incentives, values and constraints of whoever built it. That’s exactly why competition matters.

Unfortunately, based on many years of experience, I have to agree. However, I also see a light at the end of the tunnel, particularly in Europe, where there is a growing desire to break free from the US SaaS stranglehold, using open source software.

Regarding OWAI I see a fundamental difference: Slack isn’t just software, it’s a hosted service with identity, storage and network effects.

An open weight model is more like a compiler: once downloaded, nobody can revoke it. You may still pay for inference, but pricing power drops when anyone can host the same model.

Funny how »open is dangerous« only became the dominant narrative once Chinese models caught up: If your competitive moat depends on governments blocking free alternatives instead of building better products, maybe your moat isn’t technology at all.

Exactly. It’s much easier to regulate an API than software people can download and run on their own hardware. Once Since capable open weight AI models exist, they spread much like Linux or other open source software: mirrors appear, forks emerge, and improvements compound. Governments can make access less convenient, but preventing global distribution altogether is a far harder problem than restricting a hosted service.

AI companies keep promising productivity gains, but one of the first guaranteed outcomes is that everyone else helps pay the electricity bill. If the grid is upgraded for a few hyperscalers while the costs are spread across millions of customers, that’s not a free market. It’s a subsidy with extra steps.

The weird part is that everyone keeps calling it public infrastructure, but if the primary beneficiary is a handful of private AI companies, that argument starts looking pretty thin. Reliable grids benefit everyone. Dedicated power corridors for hyperscalers are a much harder sell, especially when locals bear the costs.

The awkward part is that software has a habit of racing toward free once it becomes good enough. If an open weight model delivers 95 percent of the value without recurring API costs, plenty of companies will choose that and spend the savings on integration instead. History keeps rhyming, even if investors hate the tune.

Have you finished school?

Are you still interested in Closed AI?

That’s not necessary: as the Open Weight AI models from the US are rather poor, nobody wants them anyway. 🤷

And on top of that, it’s still saddled with XTwitter’s debts.

I’d argue the models and software are the real long term assets. GPUs depreciate like any other hardware, but a better training pipeline, inference stack, proprietary data, and a model with millions of paying users can survive multiple hardware generations. The chips are replaceable. The ecosystem and customer relationships are much harder to replicate.