KolibriFly

A court should not quietly create a general duty to inspect everyone's private files because a provider could theoretically detect illegal content

Yeah, that is pretty funny considering Blackwell isn't just a chip but a whole ecosystem with a fifth-gen interconnect and huge cooling racks...

Another important thing to keep in mind here is the software. Nvidia has been optimizing their software stack for decades to squeeze every clock cycle out of the hardware under any limits. TensorRT alone does straight up magic. Apple is just starting out with MLX, and their hardware is often idling not because it is worse per watt but because the compiler does not know how to optimally load the ALU blocks for specific graphs yet

A lot of the article is about who controls the tool, who is forced to use it and who absorbs the cost when it produces bad output

I suspect "same price, three days instead of three months" is a very real win for plenty of businesses

I think the argument is that AI changes the cost of creating that groundwork, not the definition of it

The real trick is recognizing when "disposable" code has quietly become infrastructure

AI makes it cheaper to create working fragments. It does not automatically make those fragments part of a maintainable system. In practice it may make the canonization step more important not less

Everything depends heavily on the segment. Open-weight models already look good enough for a ton of internal tasks right now but there is always gonna be that small percentage of workloads where the last few percent of quality are totally worth the money.

I feel like the market is just gonna become way more mixed. Not like "everyone is switching to open" or "everyone is staying on frontier" but a mix of multiple models for different scenarios

The weakest part of the article is that the forecasts up to 2029 assume the current market structure will barely change. In three years literally everything can change, like prices, models, hardware, and how we actually use LLMs

That is exactly why big clouds never put all their eggs in one basket, even if it is a super cheap and cold basket. The cost of protecting and backing up network lines for an isolated island quickly eats up any benefits from geothermal energy. Physical security for terabit lines is way more expensive than air conditioning these days

Iceland and Norway are part of the EEA so the AI Act and GDPR will reach them just like Germany or France. Running away there from regulators makes no sense. But running from bureaucracy to get land permits and substation connections - yeah maybe municipalities work faster there

I mostly agree, though I'd phrase it slightly differently: software is becoming less of a moat by itself but not necessarily less important

The people saying "anyone can build software now" often seem to mean "anyone can generate code now"... which is not quite the same thing

Yeah, but maybe only for the visible (productized) layer of software

I feel like this is way too binary. I don't have to write every line of code myself to understand the system. I don't write my own compiler HTTP stack or database either

It's more about the level of abstraction. If AI handles 80% of the grunt work and I spend my time on architecture and reviews that's still a win

The funniest thing would be if in a couple years LLMs just end up being another checkbox next to PostgreSQL and Kubernetes

I feel like the author is jumping way too fast from "OpenAI is losing money" to "the whole AI economy is broken." A company being in the red during aggressive scaling doesn't automatically mean the unit economics don't work.

That's why a hybrid approach is needed. The agent shouldn't be making up dimensions based on an image. It should use OCR to extract the size table from the datasheet, feed it into a parametric table, and only then map it onto the base enclosure template.

Comparing it to Leetcode is completely off. On Leetcode, you're running an isolated algorithm that doesn't need any access to internal repos or staging environments. But a CI/CD sandbox that reviews PRs by definition has to have serious permissions, or it won't be able to build anything.

Even if you leave intent aside, the effect is the same: it teaches researchers that funding is conditional on staying within an invisible and shifting political boundary

And scientists are often exactly the kind of people who will try to keep going anyway

A research system can adapt to lower funding if the rules are stable. What it can't adapt to is grants being frozen, staff disappearing mid-project, forbidden vocabulary changing

A degree was never supposed to make someone instantly productive on day one. It was supposed to be paired with junior roles, mentoring, code review

I had a similar experience with unfamiliar frameworks. The less you understand the domain, the easier it is to accept clean-looking but wrong code

Good test actually : try explaining the solution without any hints. If you can't walk through why the code is structured that way, then the review was superficial

That's probably one of the most sustainable approaches. Queries like show me where to look for the problem often turn out to be more useful than just fix it for me, because they leave the most valuable part of the work to the engineer

Hardware scarcity and cloud incentives are real, but they're also part of the same broader trend: more dependency on centralized providers, less control for the buyer

Just don't design the game so that, when the business model stops working, every paid copy becomes a brick

This is exactly what is happening right now. Models are becoming more efficient but at the same time users are starting to tackle tasks they previously didn’t even try to automate.

But Jevons paradox explains the increase in consumption, but it does not necessarily answer the question of business profitability

Well, the history of cloud computing shows that infrastructure usually becomes cheaper over time. But it’s still unclear whether this rule applies to reasoning models.